Hypernetwork-Driven Model Fusion for Federated Domain Generalization

• URL: http://arxiv.org/abs/2402.06974v3
• Date: Tue, 28 May 2024 04:26:25 GMT
• Title: Hypernetwork-Driven Model Fusion for Federated Domain Generalization
• Authors: Marc Bartholet, Taehyeon Kim, Ami Beuret, Se-Young Yun, Joachim M. Buhmann,
• Abstract summary: Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data. We propose a robust framework, coined as hypernetwork-based Federated Fusion (hFedF), using hypernetworks for non-linear aggregation. Our method employs client-specific embeddings and gradient alignment techniques to manage domain generalization effectively.
• Score: 26.492360039272942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance. Traditional domain generalization aims to learn domain-invariant features, but the federated nature of model averaging often limits this due to its linear aggregation of local learning. To address this, we propose a robust framework, coined as hypernetwork-based Federated Fusion (hFedF), using hypernetworks for non-linear aggregation, facilitating generalization to unseen domains. Our method employs client-specific embeddings and gradient alignment techniques to manage domain generalization effectively. Evaluated in both zero-shot and few-shot settings, hFedF demonstrates superior performance in handling domain shifts. Comprehensive comparisons on PACS, Office-Home, and VLCS datasets show that hFedF consistently achieves the highest in-domain and out-of-domain accuracy with reliable predictions. Our study contributes significantly to the under-explored field of Federated Domain Generalization (FDG), setting a new benchmark for performance in this area.

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